ISSN 1175-5326 (print edition)Zootaxa 3825 (1): 001132 Accepted by V. Dill Orrico: 14 Apr. 2014; published: 26 Jun. 2014

ZOOTAXAISSN 1175-5334 (online edition)Copyright 2014 Magnolia Press

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ZOOTAXA

Molecular systematics of terraranas (Anura: Brachycephaloidea)

with an assessment of the effects of alignment and optimality criteria

JOS M. PADIAL1, TARAN GRANT2 & DARREL R. FROST31Section of Amphibians and Reptiles, Carnegie Museum of Natural History, 4400 Forbes Avenue, Pittsburgh, PA 15213, USA.

E-mail: padialj@carnegiemnh.org2Departamento de Zoologia, Instituto de Biocincias, Universidade de So Paulo, So Paulo, SP 05508-090, Brazil.

E-mail: taran.grant@ib.usp.br3Division of Vertebrate Zoology (Herpetology), American Museum of Natural History, Central Park West at 79th Street, New York, NY

10024, USA. E-mail: frost@amnh.org

Magnolia PressAuckland, New Zealand

3825

JOS M. PADIAL, TARAN GRANT & DARREL R. FROSTPADIAL ET AL.2 Zootaxa 3825 (1) 2014 Magnolia Press

Molecular systematics of terraranas (Anura: Brachycephaloidea) with an assessment of the effects of

alignment and optimality criteria

(Zootaxa 3825)

132 pp.; 30 cm.

26 Jun. 2014

ISBN 978-1-77557-433-0 (paperback)

ISBN 978-1-77557-434-7 (Online edition)

FIRST PUBLISHED IN 2014 BY

Magnolia Press

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ISSN 1175-5326 (Print edition)

ISSN 1175-5334 (Online edition)

Table of contentsSYSTEMATICS OF BRACHYCEPHALOIDEA

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Objectives of this study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Locus sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Taxon sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Overview and goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Optimality criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Nucleotide homology: similarity-alignment vs. tree-alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Models and model selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Maximum likelihood and Brachycephaloidea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Methods applied in this study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Tree-alignment + parsimony analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Similarity-alignment + parsimony analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Similarity-alignment + maximum likelihood analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Comparison of methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Tree-alignment + parsimony . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Similarity-alignment + parsimony . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Similarity-alignment + maximum likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Comparison of methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

The relationships and taxonomy of Brachycephaloidea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Incertae sedis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Brachycephalidae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Craugastoridae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Craugastorinae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Holoadeninae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Pristimantinae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Eleutherodactylidae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Eleutherodactylinae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Phyzelaphryninae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

APPENDIX 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

APPENDIX 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

Abstract

Brachycephaloidea is a monophyletic group of frogs with more than 1000 species distributed throughout the New World

tropics, subtropics, and Andean regions. Recently, the group has been the target of multiple molecular phylogenetic anal-

yses, resulting in extensive changes in its taxonomy. Here, we test previous hypotheses of phylogenetic relationships for

the group by combining available molecular evidence (sequences of 22 genes representing 431 ingroup and 25 outgroup

terminals) and performing a tree-alignment analysis under the parsimony optimality criterion using the program POY. To

elucidate the effects of alignment and optimality criterion on phylogenetic inferences, we also used the program MAFFT

to obtain a similarity-alignment for analysis under both parsimony and maximum likelihood using the programs TNT and

GARLI, respectively.

Although all three analytical approaches agreed on numerous points, there was also extensive disagreement. Tree-

alignment under parsimony supported the monophyly of the ingroup and the sister group relationship of the monophyletic

marsupial frogs (Hemiphractidae), while maximum likelihood and parsimony analyses of the MAFFT similarity-align-

ment did not. All three methods differed with respect to the position of Ceuthomantis smaragdinus (Ceuthomantidae),

with tree-alignment using parsimony recovering this species as the sister of Pristimantis + Yunganastes. All analyses re-

jected the monophyly of Strabomantidae and Strabomantinae as originally defined, and the tree-alignment analysis under

parsimony further rejected the recently redefined Craugastoridae and Pristimantinae.

Despite the greater emphasis in the systematics literature placed on the choice of optimality criterion for evaluating

trees than on the choice of method for aligning DNA sequences, we found that the topological differences attributable to

the alignment method were as great as those caused by the optimality criterion. Further, the optimal tree-alignment indi- Zootaxa 3825 (1) 2014 Magnolia Press 3

cates that insertions and deletions occurred in twice as many aligned positions as implied by the optimal similarity-align-PADIAL ET AL.4 Zootaxa 3825 (1) 2014 Magnolia Press

ment, confirming previous findings that sequence turnover through insertion and deletion events plays a greater role in

molecular evolution than indicated by similarity-alignments. Our results also provide a clear empirical demonstration of

the different effects of wildcard taxa produced by missing data in parsimony and maximum likelihood analyses. Specifi-

cally, maximum likelihood analyses consistently (81% bootstrap frequency) provided spurious resolution despite a lack

of evidence, whereas parsimony correctly depicted the ambiguity due to missing data by collapsing unsupported nodes.

We provide a new taxonomy for the group that retains previously recognized Linnaean taxa except for Ceuthomantidae,

Strabomantidae, and Strabomantinae. A phenotypically diagnosable superfamily is recognized formally as Brachycepha-

loidea, with the informal, unranked name terrarana retained as the standard common name for these frogs. We recognize

three families within Brachycephaloidea that are currently diagnosable solely on molecular grounds (Brachycephalidae,

Craugastoridae, and Eleutherodactylidae), as well as five subfamilies (Craugastorinae, Eleutherodactylinae, Holoadeni-

nae, Phyzelaphryninae, and Pristimantinae) corresponding in large part to previous families and subfamilies. Our analyses

upheld the monophyly of all tested genera, but we found numerous subgeneric taxa to be non-monophyletic and modified

the taxonomy accordingly.

Key words: Brachycephalidae, Craugastoridae, dynamic homology, direct optimization, Eleutherodactylidae, maximum

likelihood, missing data, Neotropics, parsimony, phylogeny, rogue taxa, sparse supermatrix, taxonomy, terrarana, wildcard

Introduction

With more than 1000 species, the clade of New World direct-developing frogs, Brachycephaloidea1, comprises around 33% of all New World frog species and nearly 17% of named anuran species worldwide (Frost, 2014). Species of this clade are found natively over a large portion of the Americas, extending from the southwestern USA to northern Argentina through a broad variety of habitats, including the cold pramos of the Andes up to 4500 m elevation, cloud forests, and lowland rainforests, as well as dry tropical scrub and even semi-arid and arid areas. These frogs are often important components of ecological communities in terms of both species composition and individual abundance (e.g., Duellman, 1978; Lynch & Duellman, 1997; Hedges et al., 2008a; Crawford et al., 2010a). As an example, in Amazonia up to 20 species of these frogs have been found at a single locality (Cisneros-Heredia, 2006).

For decades, most terraranas were considered either a tribe (Eleutherodactylini; Lynch, 1971) or subfamily (Eleutherodactylinae; Heyer, 1975; Laurent, 1986) within the disparate collection of arciferal, procoelous taxa referred to Leptodactylidae (e.g., Lynch, 1971, 1973; Heyer, 1975). The bulk of Eleutherodactylinae was grouped under the large genus Eleutherodactylus (also including most of the species now included in the nominal brachycephaloid families) but Ardila-Robayo (1979) found a paraphyletic Eleutherodactylus that would also need to include at least Barycholos, Geobatrachus, Ischnocnema, and Phrynopus to be rendered monophyletic. Izecksohn (1988) later suggested that Eleutherodactylinae was also likely paraphyletic with respect to Brachycephalidae (at that time restricted to Brachycephalus and Psyllophryne), and in support of that hypothesis Pombal (1999) noted that the only frogs known to possess an egg tooth were eleutherodactylines and Brachycephalus. Subsequently, Darst & Cannatella (2004) inferred via molecular data that the "leptodactylid" subfamily Eleutherodactylinae was indeed paraphyletic with respect to Brachycephalidae (i.e., Brachycephalus [by that time including Psyllophryne]), although they explicitly did not make the nomenclatural remedy. It was Dubois (2005a), following Darst & Cannatellas (2004) results, who first united eleutherodactylines and Brachycephalusinto a single family-group, placing Eleutherodactylinae into the synonymy of a subfamily of Leptodactylidae: Brachycephalinae.

Although Dubois (2005a) action resolved the paraphyly of Eleutherodactylinae, it perpetuated the non-monophyly of Leptodactylidae. Based on an analysis of previously published evidence combined with a large amount of new DNA sequences from species now placed in the genera Barycholos, Brachycephalus, Craugastor, Eleutherodactylus, Haddadus, Ischnocnema, Oreobates, Psychrophrynella, and Pristimantis, Frost et al. (2006) removed all terraranas from Leptodactylidae and recognized them as Brachycephalidae. Their results provided decisive support for the monophyly of the group, as previously evidenced by the phenotypic synapomorphies of

1. We herein refer the clade of New World direct-developing frogs to Brachycephaloidea Gnther, 1858 (equivalent in diagnosis and content to Brachycephalidae sensu Frost et al., 2006) and use terrarana (pl. terraranas) of Hedges et al.(2008a) as the common name for frogs in this clade.

direct development (Lutz, 1954; Gallardo, 1965; Lynch, 1971), the presence of a single, bicuspid, keratinized egg SYSTEMATICS OF BRACHYCEPHALOIDEA

tooth in embryos (Sampson, 1904; Noble, 1926; Pombal, 1999), and T-shaped terminal phalanges (Lynch, 1971). More recently, the monophyly of the group has again received substantial support from morphology in the form of seven additional synapomorphies in the urogenital and vascular anatomy (Taboada et al., 2013).

The monophyly of terraranas has been most decisively tested, and corroborated, by extensive analyses of DNA sequences for a large proportion of the group in the studies of Heinicke et al. (2007) and Hedges et al. (2008a), with 276 and 346 terminal species of terraranas, respectively. Hedges et al. (2008a) provided a new family-level taxonomy designed to make the taxonomy of Brachycephalidae of Frost et al., (2006) (> 850 species at the time) more manageable by splitting the group into four families (p. 11). Rather than employ the available family-group name Brachycephaloidea Gnther, 1858 for the clade containing the new families, Hedges et al. (2008a) proposed the new unranked name Terrarana explicitly to avoid putting in place yet another formal name (superfamily rank) and the potential problems it might raise in dealing with existing superfamily names (e.g., Hyloidea) that may apply to this group (p. 11). We do not find these arguments to be compelling for the following reasons.

First, with nearly half of all frogs (> 3600 of ca. 7200 species and ca. 20 families) currently placed within Hyloidea, the usefulness of the superfamilial taxonomy of frogs employed by Hedges et al. (2008a) is limited due to the over inclusiveness of regulated family-group names. Hyloidea has been redelimited several times as opinions changed (e.g., Duellman, 1975 [as Bufonoidea], Darst & Cannatella, 2004; Pyron & Wiens, 2011) and remains a huge and unstably delimited taxon whose rank formality precludes the nomenclatural recognition needed for groups between it and the large number of formal families that it contains. A solution to this problem was provided by Frost et al. (2006), who explicitly deployed a number of unranked above-family-group taxon names in order to provide taxonomists with room for maneuver so that a stable family-group taxonomy could be built from the

ground up2. We therefore fail to see the benefit of avoiding a formal family-group name for this important and universally recognized group. Indeed, Hedges et al. (2008a) went on to treat terraranas formally as a New Taxon (see also Hedges et al., 2008b; Heinicke et al., 2009) and, as such, the consequence of this act was not to provide an informal name for this group, but rather a formal but unregulated and unranked name, Terrarana, even though (or perhaps because) the regulated family-group name Brachycephaloidea was already available.

Second, problems in dealing with existing taxa are by no means avoided by naming a taxon in a way that avoids regulation by the International Code of Zoological Nomenclature (1999). Indeed, the Code and its Commission exist precisely to resolve problems, should they arise. This is not to say that we think all unregulated

names are undesirable or that the Code should regulate above-family-group taxa3. To the contrary, we believe unregulated names play an important role in allowing workers to discuss species and their relationships. Whether ranked or unranked, unregulated names for more inclusive clades are necessary once family-group names have been exhausted at less inclusive hierarchic levels, and they can be extremely useful even when family-group names have not been exhausted. For example, standard vernacular names provide stability as scientific hypotheses are proposed and refuted (Crother, 2009), and informal groups (e.g., species groups) can allow systematists to recognize and discuss groups tentatively as evidence accumulates without proliferating taxonomy with yet another formal name. Indeed, given the recentness and limited and conflicting evidence for the major lineages within this clade, it would have been understandable if Hedges et al. (2008a) had used informal, unregulated names for the putative major lineages within the clade instead of dividing them into four formal families (two of which have already been combined; Pyron & Wiens, 2011).

In contrast, recognition of the Brachycephaloidea clade is by no means tentative. The group has been recognized more-or-less universally since Lynch (1971) modified earlier proposals by Lutz (1954) and Gallardo (1965), the only noteworthy recent change being the inclusion of Brachycephalus, which is why Hedges et al.(2008a) were warranted in applying a formal name to the inclusive group, their choice being the unranked Terrarana (instead of Brachycephalidae or Brachycephaloidea).

Concerning the division of the group into several families, Hedges et al. (2008a) restricted Brachycephalidae to

2. We reject the use of the family-group name Hyloidea for a taxon otherwise equating to the above-family-group name Notogaeanura of Frost et al. (2006), which equates to Hyloidea of Pyron & Wiens (2011), and differs in content from Hyloides of Frost et al., (2006), only in the latter's exclusion of Sooglossidae and Nasikabatrachidae.

3. We do not adopt the suggested rules for regulating above-family-group names by Dubois (2005b, 2006), which in our view are problematic and, moreover, have no force pending discussion and adoption by the International Commission of Zoological Nomenclature. Zootaxa 3825 (1) 2014 Magnolia Press 5

the clade composed of Brachycephalus and Ischnocnema and extracted another three families and four subfamilies PADIAL ET AL.6 Zootaxa 3825 (1) 2014 Magnolia Press

from Brachycephalidae sensu Frost et al. (2006), all posited on molecular grounds to be monophyletic: Craugastoridae, Eleutherodactylidae (including Phyzelaphryninae and Eleutherodactylinae), and Strabomantidae (including Holoadeninae and Strabomantinae). Subsequently, Heinicke et al. (2009) named a species from the Guiana Shield previously thought to be related to species of Pristimantis but recovered as the sister of all other terraranas by Hedges et al. (2008a; listed by them as "Unknown anuran sp"). In order to preserve the families Hedges et al. (2008a) had just recognized, Heinicke et al. (2009) named the genus Ceuthomantis and family Ceuthomantidae to accommodate that species (C. smaragdinus) and two others previously referred to Pristimantisbut assumed on the basis of morphological similarity to be closely related.

In their large study of legacy DNA sequences, Pyron & Wiens (2011) recovered a topology that required a number of changes to the taxonomy proposed by Hedges et al. (2008a), the most conspicuous being that Strabomantidae of Hedges et al. (2008a) was non-monophyletic with part (Strabomantis) more closely related to Craugastoridae (Craugastor and Haddadus). To avoid partitioning terraranas into additional families, Pyron & Wiens (2011) placed Strabomantidae into the synonymy of Craugastoridae, rendering Strabomantinae a monotypic subfamily (containing only the genus Strabomantis) and proposed the nomen nudum Pristimantinae [later diagnosed and validated by Ohler & Dubois (2012)] for the clade of Lynchius, Oreobates, Phrynopus, and Pristimantis, but excluding Yunganastes. Under this new arrangement, four families were recognized: Brachycephalidae, Ceuthomantidae, Craugastoridae, and Eleutherodactylidae.

Craugastoridae sensu Pyron & Wiens (2011) was not recognized by Blackburn & Wake (2011), who retained the scheme of Hedges et al. (2008a), arguing that because of low support values among basal nodes in this larger clade, the analysis of Pyron & Wiens (2011) does not reject the hypothesis that Craugastoridae is sister to the Strabomantidae (p. 41, fn. 24). However, insofar as that clade is absent from their optimal maximum likelihood tree, the analysis of Pyron & Wiens (2011) does reject that hypothesis, regardless of the bootstrap values. Further, in the Hedges et al. (2008a) results Strabomantidae and Strabomantinae present bootstrap frequencies < 70% in all analyses, and the sister relationship of Craugastoridae and Strabomantidae was rejected in two of their three analyses (the 362- and 216-taxa analyses; 54% bootstrap in likelihood and unsupported in parsimony in the third one, the 80-taxon analysis). Despite increased character sampling, resampling values were even lower in the tree proposed by Heinicke et al. (2009), which also recovered Strabomantinae sensu Hedges et al. (2008a) as non-monophyletic. As such, it was no surprise that the denser taxon sampling of Pyron & Wiens (2011) would result in topological changes, and it is evident that Blackburn & Wakes (2011) preference of the taxonomy of Hedges et al.(2008a) over that of Pyron & Wiens (2011) was not due to rejection of clades with low resampling values per se, but something else.

In summary, in less than a decade the taxonomy of terraranas has shifted from having its parts placed in two distantly related families (Brachycephalidae [composed only of Psyllophryne and Brachycephalus] and Leptodactylidae [the subfamily Eleutherodactylinae, with more than 700 species in a single genus, Eleutherodactylus]), to being merged into a single large, but monophyletic subfamily (Brachycephalinae; Dubois, 2005a) and then family (Brachycephalidae; Frost et al., 2006), then referred to the unranked taxon Terrarana and partitioned into four families (Hedges et al., 2008a), then five (Heinicke et al., 2009), then four (Pyron & Wiens, 2011), and then five again (Blackburn & Wake, 2011). Additionally, five subfamilies were proposed during this period, and species have been transferred across families as DNA sequences have accumulated (e.g., Canedo & Haddad, 2012). Few groups of amphibians have enjoyed such dramatic taxonomic instability in recent times.

Beyond the family-group changes promoted by new data, new analyses, and conflicting views on best taxonomic practice, both understanding and conflict have increased among genera as well. Adelophryne, Holoaden, Noblella, Phyzelaphryne (Heinicke et al., 2007; Hedges et al., 2008a), Yunganastes (Padial et al., 2009), and Euparkerella (Canedo & Haddad, 2012) were corroborated as terraranas, and six more genera (Bryophryne, Diasporus, Haddadus, Psychrophrynella, Isodactylus [preoccupied by Isodactylus Gray, 1845; replaced by Hypodactylus Hedges et al., 2008b], and Lynchius) were proposed by Hedges et al. (2008a), partitioning and redelimiting the large South American groups of Eleutherodactylus and Phrynopus of earlier authors (e.g.,Lynch, 1971, 1975; Lynch & Duellman, 1997; Frost et al., 2006).

Despite the advances brought by molecular data in understanding amphibian higher systematics, the position of terraranas within Nobleobatrachia remains conflicted. Faivovich et al. (2005) and Frost et al. (2006) found Brachycephaloidea to be the sister taxon of a large clade including Hemiphractidae (in the case of Frost et al.,

2006, all except Hemiphractus), Hylidae, Bufonidae, Leptodactylidae, and others. Roelants et al. (2007), using SYSTEMATICS OF BRACHYCEPHALOIDEA

different and fewer terminals, substantially different molecular data, and analyzing a similarity-alignment with a maximum likelihood method, found terraranas to be embedded within hylids. Wiens et al. (2005), employing Bayesian and parsimony analyses of a combined morphology and DNA dataset, found hemiphractids (Cryptobatrachus, Flectonotus, Gastrotheca, Hemiphractus, and Stefania) to form the sister group of a clade of terraranas, including the genera Oreobates, Pristimantis, Lynchius, and Strabomantis (following the current taxonomy). Heinicke et al. (2009) subsequently included representatives of 13 genera of brachycephaloids, three genera of hemiphractids (Flectonotus, Hemiphractus, and Stefania), and representatives of all other families of Nobleobatrachia of Frost et al. (2006) and again recovered hemiphractids and brachycephaloids as sister groups (both under parsimony and maximum likelihood), forming the most derived clade of Nobleobatrachia Frost et al.(2006), a relationship that they formalized with the unranked name Orthobatrachia. More recently Pyron & Wiens (2011) found Brachycephaloidea to be the sister of all other nobleobatrachians in their maximum likelihood analysis, a result concordant with the results of the much smaller study of Darst & Cannatella (2004).

Pyron & Wiens (2011) performed the largest phylogenetic (maximum likelihood) analysis of Brachycephaloidea to date and provided a jumping-off point for additional phylogenetic work. Nevertheless, several loci sampled by Frost et al. (2006), Hedges et al. (2008a), and Heinicke et al. (2009) were not included by them, several terminals were excluded, erroneous identifications of GenBank sequences were perpetuated or evidenced in their analyses (e.g., Blotto et al., 2012; see also below), and a substantial number of new sequences for additional taxa have accumulated subsequently. What is more important than these kinds of shortcomings, for which any large study can be criticized, is that the degree to which the similarities and differences between their results and those of previous studies depend on the underlying assumptions of each of the methods of optimization and sequence-alignment is unclear.

Objectives of this study

The primary objective of this study is to identify the optimal phylogenetic explanation of the species diversity of Brachycephaloidea. To provide the strongest possible test of the monophyly of Brachycephaloidea and its component subclades, we analyzed published (and, where necessary, reidentified) DNA sequences for all species listed in GenBank as of February 2012. Because all terminals and all molecular data used by previous workers (e.g., Heinicke et al., 2007; Hedges et al., 2008a, Heinicke et al., 2009; Pyron & Wiens, 2011) are included, our study represents a test of all previous large-scaled molecular-based hypotheses of relationship. New sequences from two recent large studies focusing on parts of the group (Canedo & Haddad, 2012; Pinto-Snchez et al., 2012), studies focusing on smaller parts of the terrarana tree (Amaro et al., 2013; Barrio-Amors et al. 2013; Fouquet et al., 2012; Fusinatto et al., 2013; Rodrguez et al., 2013; Zhang et al., 2013), dealing with species-level taxonomy (Brusquetti et al., 2013; Gehara et al., 2013; Hertz et al., 2013: Fouquet et al., 2013a; Garca-R. et al., 2014; Pereyra et al., 2014) or phylogeography (Rodrguez et al., 2012; Garca-R. et al., 2012; Kieswetter & Schneider, 2013) were not available in time for this study. Nonetheless, they differ little or not at all from our results, which we address in detail below.

A secondary, albeit equally important, objective of this study is to discern the effects that increasing assumptions about both nucleotide homology and evolutionary processes have on phylogenetic inferences. Specifically, we compare the results of tree-alignment under parsimony (the optimality criterion being the minimization of hypothesized changes required to explain the observed variation in DNA sequences, also referred to as direct optimization or dynamic homology; Sankoff, 1975; Wheeler, 1996, 2001; Wheeler et al., 2006; Grant & Kluge, 2009) with results from state-of-practice maximum likelihood analyses that extend from a prior, similarity-based alignment and assume a probabilistic model of molecular evolution (see Felsenstein, 2004). In order to distinguish between effects of alignment and tree selection criteria, we also analyze the same similarity-based alignment under parsimony. Nevertheless, for reasons discussed below, we consider the tree-alignment + parsimony solution to be optimal and use it for taxonomic decisions. Zootaxa 3825 (1) 2014 Magnolia Press 7

MaterialPADIAL ET AL.8 Zootaxa 3825 (1) 2014 Magnolia Press

Locus sampling

Phylogenetic analyses in this study employ DNA sequences of terraranas for 22 loci available in GenBank as of February 1, 2012, and which represent all loci used by previous studies to infer relationships of Brachycephaloidea. Non-coding mtDNA genes include rRNA genes of the heavy strand transcription unit 1 fragment (12S, 16S and the

intervening tRNAvaline, and tRNAleucine segments). Protein-coding mtDNA genes include cytochrome b (cytb), cytochrome c oxidase subunit I (COI), and NADH dehydrogenase subunit I (ND1) and subunit II (ND2), and

intervening tRNAcyst. Nuclear protein-coding genes include two exons of cellular myelocytomatosis (c-myc), chemokine receptor 4 (CXCR4), histone H3 (HH3), sodium-calcium exchanger 1 (NCX1), propiomelanocortin A (POMC), recombination-activating protein 1 (RAG1), rhodopsin (Rhod), seven-in-absentia (SIA), solute carrier family 8 member 3 (SLC8A3), and tyrosinase precursor (Tyr). Non-coding nuclear genes include 28S and the intron region of the cellular myelocytomatosis gene (c-myc). Accession numbers for all sequences used in this study are listed in Appendix 1.

Taxon sampling

DNA sequences represent 456 terminals (Appendix 1), of which 25 are treated as outgroup taxa. Outgroup sampling was guided by results of the following recent phylogenetic analyses: Darst & Cannatella's (2004) parsimony analyses recovered terraranas as the sister group of their sample of Nobleobatrachia, while in their maximum likelihood analyses they were embedded within Nobleobatrachia in an unresolved position. Faivovich et al. (2005) found hemiphractids to be paraphyletic with respect to Brachycephaloidea because two species of Eleutherodactylus (now Pristimantis pharangobates and P. thymelensis) formed the sister group of Hemiphractus helioi, and the inclusive clade was placed with Brachycephalus ephipppium and Phrynopus sp. (now Psychrophrynella guillei). Wiens et al. (2005) found hemiphractids (Cryptobatrachus, Hemiphractus, Flectonotus, Gastrotheca, and Stefania) to be sister to a clade of terraranas including, following the current taxonomy, the genera Lynchius, Oreobates, Pristimantis, and Strabomantis. Frost et al. (2006) found terraranas to be the sister of all nobleobatrachians except Hemiphractus helioi (and excluding other species now included in Hemiphractidae), a clade they formally recognized as Meridianura. Roelants et al. (2007) found Brachycephaloidea to be embedded within hylids. Heinicke et al. (2009) sampled 13 genera of terraranas, three genera of hemiphractids (Flectonotus, Hemiphractus, and Stefania), and an ample representation of genera of the Nobleobatrachia and Australobatrachia of Frost et al. (2006), and again recovered Hemiphractidae and Brachycephaloidea as sister groups. Pyron & Wiens (2011) recovered Brachycephaloidea as the sister of all other nobleobatrachians, a result concordant with the parsimony analyses of Darst & Cannatella (2004). The analyses by Zhang et al. (2013) of nearly complete mtDNA genomes found three brachycephaloid terminals (Craugastor augusti, Eleutherodactylus atkinsi, and Pristimantis thymelensis) as the sister group of nobleobatrachians. Fouquet et al. (2013b) found a clade with representatives of six genera of terraranas (Brachycephalus, Craugastor, Eleutherodactylus, Oreobates, Phyzelaphryne, and Pristimantis) as either the sister group of a diverse array of nobleobatrachians in Bayesian analyses or embedded within Nobleobatrachia as the sister of a group of hemiphractids (Gastrotheca, Hemiphractus, and Stefania) in maximum likelihood analyses. The same study used a larger array of terraranas (Brachycephalus ephippium, Ceuthomantis smaragdinus, Eleutherodactylus marnockii, Haddadus binotatus, Phyzelaphryne miriamae, and Pristimantis pharangobates misidentified as P. pluvicanorus [now in Yunganastes]) for their maximum parsimony and tree-alignment inferences and found terraranas to be paraphyletic, with Ceuthomantis as the sister group to all other terraranas and nobleobatrachians. Accordingly, we included 23 species of Nobleobatrachia representing all of the groups previously hypothesized to be closely related to Brachycephaloidea (13 species of hemiphractids, 8 species of hylids, and 2 species of leptodactylids) plus the distantly related Calyptocephalella gayi(Calyptocephalellidae) and Xenopus laevis (Pipidae) as the root. The identities of three outgroup terminals were corrected (Table 1).

The ingroup includes 431 terminals representing 19 nominal genera (Barycholos, Brachycephalus, Bryophryne, Diasporus, Ceuthomantis, Craugastor, Eleutherodactylus, Haddadus, Holoaden, Hypodactylus, Ischnocnema, Lynchius, Noblella, Oreobates, Phrynopus, Pristimantis, Psychrophrynella, Strabomantis, and Yunganastes), 408 nominal species and 23 unidentified species. Due in part to the difficulties involved in identifying terraranas and in part to the rapid evolution of understanding of the group, the identities of numerous

samples used in previous phylogenetic analyses had to be corrected. Of the 431 terminals, 24 GenBank sequences SYSTEMATICS OF BRACHYCEPHALOIDEA

required re-identification (Table 1) and another 23 could not be identified beyond the generic level (Appendix 1). Corrections were made by cross-checking GenBank identifications with updated determinations provided in the publications for which sequences were originally submitted, new identifications provided in subsequent literature, and by direct examination of voucher specimens by the first author, and in one case based on the results of our phylogenetic analyses. Unfortunately, in this process we overlooked two nominal species of Eleutherodactylus (E. diplasius, E. notitodes) that were elevated from subspecies to species by Hedges et al. (2008a) because corresponding sequences were deposited in GenBank under their older covering-species names E. wetmorei and E. audanti. Similarly, we overlooked E. varians because among the several sequences deposited in GenBank under this name several correspond to E. olibrus (formerly a subspecies of E. varians) and we only sampled those.

TABLE 1. Updated terminal species names of GenBank sequences re-identified for the purposes of this study.

Terminal name Original name and rationale for re-identification

Adelophryne patamona Adelophryne adiastola (ROM 39578) of Hedges et al. (2008a) is A. patamona according to Fouquet et al. (2012, p. 555).

Craugastor cf. augusti Craugastor augusti from Alamos, southern Sonora, Mexico (DQ283271) of Frost et al. (2006) is treated here as C. cf. augusti in contrast to another terminal, C. augusti from Jalisco (UTACV A-12980), which comes from a population geographically much closer to the type locality (Guanajuato, Mexico) than to the Sonoran locality, and this nominal species likely represents a species complex (Goldberg et al., 2004).

Craugastor cf. longirostris Craugastor cf. longirostris (FMNH 257678) of Streicher et al. (2009) and Craugastor aff. longirostris (AJC-2009) of Crawford et al. (2010a) are considered conspecific following Crawford et al. (2010a), with sequences of both specimens being used for our terminal C. cf. longirostris.

Craugastor montanus Sequences of Craugastor sartori (EF493530, EF493478, EF493453, AY273121, and AY211308) are re-identified as C. montanus because the formeroriginally a replacement name for Microbatrachylus montanus Taylor, 1942 (when Microbatrachylus montanus was Eleutherodactylus)is now considered a junior synonym of the later (see Frost, 2014).

Cryptobatrachus fuhrmanni Cryptobatrachus sp. (JDL14865) of Darst and Cannatella (2004) is Cryptobatrachus fuhrmanni (S. Castroviejo, personal commun.; J. D. Lynch in litt. to W.E. Duellman, the latter in litt. to S. Castroviejo)

Diasporus citrinobaephus Diasporus aff. diastema of Crawford et al. (2010a) is considered here as D. citrinobaephus because Hertz et al. (2012, p. 33) found the former to be sister to topotypic populations of the latter, show differences of 1.8% in base composition in 16S sequences, and they occur within the same habitat.

Fritziana aff. fissilis Flectonotus sp. (CFBH5726 [the number CFBH5720 listed in GenBank and the original publication is erroneous]) of Faivovich et al. (2005) is a species of Fritziana following the partition of Flectonotus by Duellman et al. (2011, p. 25), and according to C. F. B. Haddad (personal commun.) it represents an unnamed species related to F. fissilis.

Gatrotheca piperata Gastrotheca cf. marsupiata (MNK 5286) of Faivovich et al. (2005) was re-determined as G. piperata by Duellman and Khler (2005).

Oreobates saxatilis Ischnocnema sp. (DQ284091, DQ283788, DQ282661) of Frost et al. (2006) is Oreobates saxatilis according to Padial et al. (2012, p. 11).

Phrynopus auriculatus Phrynopus sp. (KU 291633) of Heinicke et al. (2007) is reidentified as P. auriculatus following Duellman and Hedges (2007).

Phrynopus tribulosus Phrynopus sp. (KU 291630) of Hedges et al. (2008a) is herein reidentified as P. tribulosus (see Duellman and Hedges, 2007).

Pristimantis achuar Pristimantis ockendeni (QCAZ 25273) corresponds to P. achuar (see Elmer and Cannatella, 2008).

......continued on the next page Zootaxa 3825 (1) 2014 Magnolia Press 9

TABLE 1. (Continued)PADIAL ET AL.10 Zootaxa 3825 (1) 2014 Magnolia Press

Terminal name Original name and rationale for re-identification

Pristimantis adiastolus Eleutherodactylus sp. (KU 291681) of Hedges et al. (2008a) corresponds to Pristimantis adiastolus (see Duellman and Hedges, 2007).

Pristimantis albertus Pristimantis sp. SBH-2008 (KU 291675) of Hedges et al. (2008a) corresponds to P. albertus (see Duellman and Hedges, 2007).

Pristimantis altammnis P. ockendeni (QCAZ 25439) corresponds to P. altammnis (see Elmer and Cannatella, 2008).

Pristimantis aniptopalmatus Sequences available in GenBank as Pristimantis sp. SBH-2008 voucher KU 291666 are here identified as P. aniptopalmatus because they cluster in our analyses with a paratype of P. aniptopalmatus and are topotypic. These sequences are listed in GenBank as produced by Hedges et al. (2008a), but there is no reference to that terminal or its accession numbers in Hedges et al. (2008a). Duellman and Hedges (2005) described and named P. aniptopalmatus, produced sequences for one paratype and two referred specimens for their molecular phylogenetic analyses, but did not deposit sequences in GenBank. Later Heinicke et al. (2007) used sequences of one paratype and deposited sequences in GenBank (identified as Pristimantis aniptopalmatus voucher KU 291627 in GenBank), which are also used herein. Therefore, two terminals in our trees correspond to P. aniptopalmatus.

Pristimantis cruciocularis Pristimantis sp. SBH-2008 (KU 291673) of Duellman and Hedges (2005) corresponds P. cruciocularis (see Lehr et al., 2006).

Pristimantis festae Pristimantis trepidotus of Heinicke et al. (2007) has been considered a synonym of P. festae since Lynch (1974) and we use the latter name.

Pristimantis kichwarum P. ockendeni (QCAZ 18069) corresponds to P. kichwarum (see Elmer and Cannatella, 2008).

Pristimantis minutulus Pristimantis sp. SBH-2008 (KU 291677) of Hedges et al. (2008a) corresponds to P. minutulus (see Duellman and Hedges, 2007).

Pristimantis ornatus Pristimantis cf. rhabdolaemus SBH-2008 (MTD 45073) of Duellman and Hedges (2005) corresponds P. ornatus (see Lehr et al., 2006).

Pristimantis pharangobates Yunganastes pluvicanorus (AMNH-A 165195) of Faivovich et al. (2005) was reidentified as P. rhabdolaemus by Padial et al. (2007, p. 235), but the corresponding population is now assigned to P. pharangobates according to Duellman and Lehr (2009, p. 215), who removed it from the synonym of P. rhabdolaemus where it had been placed by Lynch and McDiarmid (1987).

Pristimantis reichlei Pristimantis peruvianus of Hedges et al. (2008a) is P. reichlei according to our examination of the voucher specimen (MHNSM 9267) (see also Padial and De la Riva, 2009).

Pristimantis saltissimus Pristimantis sp. SBH-2008 (ROM 43310) of Hedges et al. (2008a) corresponds to P. saltissimus (see Means and Savage, 2007).

Pristimantis simonsii Phrynopus simonsii (KU 212350) of Wiens et al. (2005) is Pristimantis simonsii according to Hedges et al. (2008a, p. 125).

Pristimantis sp. (ROM 43978) Pristimantis zeuctotylus of Hedges et al. (2008a) is here treated as Pristimantis sp. (ROM 43978) based on examination of the voucher.

Psychrophrynella guillei Phrynopus sp. (AMNH-A 165108) of Faivovich et al. (2005) is Psychrophrynella guillei (see De la Riva 2007, p. 258).

Psychrophrynella saltator Phrynopus sp. GF-La_Paz-Phr1 of Lehr et al. (2005) is Psychrophrynella saltator according to the results of De la Riva et al. (2008).

Psychrophrynella usurpator Phrynopus peruvianus (KU 173495) of Heinicke et al. (2007) is Psychrophrynella usurpator according to the results of De la Riva et al. (2008).

MethodsSYSTEMATICS OF BRACHYCEPHALOIDEA

Overview and goals

We are in a period of enormous growth of phylogenetic knowledge. To a large degree, this growth has been driven by technological advances in obtaining and analyzing DNA sequences that enable workers to perform sophisticated analyses without requiring that they understand the underlying logical and theoretical foundations of those analyses. The positive aspects of the resulting increased population of workers cannot be overstated. It is good that more people are generating data and publishing trees, even in a rough-and-ready form. The downside, however, is that in such a climate it is social trends, instead of intellectual discussion, that largely govern which techniques are popular and correct (Kuhn, 1962), and much of the discourse moves away from science and towards propaganda and sloganeering. Sober (2004) drew attention to this in regards to the misconception that maximum likelihood is for DNA and parsimony is for phenotypic characters, but otherwise such sociological aspects of systematics are rarely discussed in scientific circles (but see Frost et al., 2008).

The effect of social pressures is perhaps best exemplified by the trend of empirical papers to base conclusions on the combined or cherry-picked results of whatever methods (e.g., maximum likelihood, Bayesian inference, parsimony) and software are currently popular, despite their incongruent assumptions and without explaining why other, previously popular methods (e.g., neighbor joining, UPGMA) or variations (e.g., implied weighting in parsimony) were not explored as well. To be clear, where the objectives are methodological, comparisons of results from different methods can provide insights into the effects methods and their assumptions have on empirical inferences (e.g., the extent to which increased assumptions cause results to depart from the most parsimonious explanation). That is, like numerical simulations (Oreskes et al., 1994), such comparisons are heuristic, but they do not constitute empirical tests because there is no logical basis for employing congruence or incongruence of results across analytical methods as an optimality criterion (Grant & Kluge, 2003). Unfortunately, this path seems most often to be taken in order to avoid having to choose and defend a particular method and, thus, controversy. Regardless, both philosophically and methodologically, by deciding not to choose, a choice still has been made.

As noted above, one of our objectives is to relate the similarities and differences of the trees that result from different methods of phylogenetic analysis to the underlying assumptions and procedures of these approaches. Specifically, we compare the trees selected through tree-alignment (here used as synonymous with dynamic homology analysis: Sankoff, 1975; Wheeler et al., 2006; Varn & Wheeler, 2012) under the optimality criterion of parsimony with those found through analysis of a prior, static, similarity-based alignment analyzed under both the maximum likelihood and parsimony optimality criteria. Although we attribute incongruence to specific analytical causes as precisely as possible, to identify the exact cause of each and every difference would require analytical manipulations of each assumption and combination of assumptions of each method, which is beyond the scope of this paper due to the size of the dataset and available computational resources. Instead, we draw attention to the importance of these assumptions through a comparison of three broadly different but overlapping methods of analysis. Similarly, much of our discussion below applies equally to Bayesian phylogenetic inference, but for simplicity we limit our comparisons to parsimony and maximum likelihood.

Below we clarify the philosophical and theoretical foundations of the competing approaches with special reference to 1) parsimony and maximum likelihood optimality criteria; 2) tree-alignment and phylogenetic analysis of similarity-alignments; and 3) the use of models of molecular evolution to infer historical events.

Optimality criteria

Although optimality criteria are usually discussed in terms of the objective functions they minimize or maximize, their preference is based on underlying philosophical and theoretical foundations. As employed in phylogenetics, parsimony and maximum likelihood extend from fundamentally different foundations, despite their numerical equivalence in certain situations (e.g., Goloboff, 2003). Parsimony is a non-statistical, non-parametric, evidentially conservative approach to scientific inference that aims to maximize explanatory power by minimizing assumptions about both the process of character evolution and the quantity of evolutionary events needed to explain the data (Eernisse & Kluge, 1993; Kluge & Grant, 2006; Grant & Kluge, 2009). As a scientific method, its justification is based on refutationism sensu Popper (1959, 1963, 1972, 1983; for its application to phylogenetic inference see Wiley, 1975; Farris, 1983; Farris et al., 2001; Kluge, 2001, 2009) as applied to historical inferences, whereby the least refuted hypothesis is selected as optimal. Operationally, the nested patterns of homologs are interpreted as a Zootaxa 3825 (1) 2014 Magnolia Press 11

retrodictive map of history, with the optimal tree being that which requires the fewest transformation events to PADIAL ET AL.12 Zootaxa 3825 (1) 2014 Magnolia Press

explain the evidence in light of background knowledge (Kluge & Grant, 2006; Grant & Kluge, 2009). Background knowledge in this context is limited only to those assumptions that are necessary to make an inference of common ancestry, i.e., descent with modification (Hennig, 1966; Kluge, 1999).

In contrast, maximum likelihood is a statistical, parametric, evidentially ambivalent approach that aims to maximize accuracy by incorporating a potentially unlimited number and diversity of assumptions about the process of evolution (e.g., Felsenstein, 2004). Given that evolutionary history is unknown, the accuracy of hypothesized phylogenetic hypotheses cannot be assessed. However, an enormous number of numerical simulation studies have been undertaken to prove the accuracy of maximum likelihood methods for phylogenetic inference (e.g., Hillis, 1995; Huelsenbeck, 1995; Philippe et al., 2005; Swofford et al., 2001; but see e.g., Siddall, 1998; Farris, 1999; Pol & Siddall, 2001; Kolaczkowski & Thornton, 2004; Kck et al., 2012), the results of which are extended by induction to empirical studies, despite the well established pitfalls of such reasoning (e.g., Oreskes et al., 1994; Grant, 2002; Grant & Kluge, 2003). As such, in phylogenetics, maximum likelihoods justification falls within the realm of verificationism (Siddall & Kluge, 1997), although, in practice, it often shifts to instrumentalism (see below).

Parsimony and maximum likelihood also entail contradictory views of history and historical inference. Historical inference under parsimony is idiographic in that it aims to infer particular events rather than universal trends or laws and, as such, treats all hypothesized homologs and evolutionary transformations as unique, concrete, and singular (i.e., as ontological individuals; Grant & Kluge, 2004, 2009; Kluge & Grant, 2006). Insofar as infrequent events must have occurred in the past, the frequency of a class of events (e.g., transitions) has no bearing on the inference of a particular historical event (e.g., a transition in position 384 of the cytochrome b gene in the most recent common ancestor of Pristimantis). By using the overall frequency of classes of events (within some arbitrarily circumscribed universe) to infer the past occurrence of particular events, maximum likelihood necessarily assumes that evolutionary history can be reduced to universal probabilistic laws applied to classes of events (e.g., transitions, transversions, insertions, deletions; for general discussions of this fallacy and its effects outside systematics see Popper, 1959; Taleb, 2007). Moreover, maximum likelihood conflates the probability that a class of event could have occurred with the probability that a particular event did occur. Making matters more complicated, the frequency of events can only be estimated a posteriori once all the particular events have been counted. In other words, the frequency of events is a result of phylogenetic analysis, not a premise (Sankoff et al., 1973; 1976; Farris, 1983) and, hence, this frequentist approach to historical inference is logically flawed.

Despite the contradictory logical foundations of parsimony and maximum likelihood, much effort has gone into portraying parsimony as if it were a parametric statistical method by identifying the assumptions under which the maximum likelihood solution is identical to the parsimony solution. The resulting parsimony-equivalent likelihood models (reviewed and discussed by Holder et al., 2010; Steel, 2011) purport to expose parsimonys implicit statistical assumptions about the evolutionary process. Although this line of reasoning has a long and impressive pedigree dating back at least four decades (Farris, 1973; Felsenstein, 1973) and has played a major role in methodological debates, in the final analysis it has generated much more heat than light, principally because it rests on the false premise that all quantitative, numerical methods are necessarily statistical, even if only implicitly. To the contrary, finding that a maximum likelihood solution under a particular model (be it simple or complex) matches a parsimony solution has no logical bearing on the justification of the non-probabilistic method of parsimony and its assumptions. (Similarly, mathematical formulas generating, respectively, a parabola and a straight line on a Cartesian plane cannot be judged identical even if they produce the same formulaic results at two points of intersection.) Further, the fact that both extremely complex (e.g., Farris, 1973; Tuffley & Steel, 1997) and simple (e.g., Goldman, 1990) parsimony-equivalent models have been identified (and more undoubtedly exist; Sober, 2004) demonstrates the futility of this approach, even if parsimony is interpreted as a statistical method (Goloboff, 2003).

Nucleotide homology: similarity-alignment vs. tree-alignment

The importance of alignment in the phylogenetic analysis of DNA sequences cannot be overstated; it truly is the elephant in the room with respect to molecular phylogenetics. The number of possible alignments for even a tiny number of terminals and nucleotides is staggering and increases faster than the number of possible trees (Slowinski, 1998). Many methods to select optimal alignments have been proposed, and different alignment methods can lead to different alignments and different alignments can lead to different phylogenetic trees

(Wheeler, 1994; Morrison & Ellis, 1997; Whiting et al., 2006; Wong et al., 2008; Blackburne & Whelan, 2012). As SYSTEMATICS OF BRACHYCEPHALOIDEA

we argue below, which method is best depends on the investigators goals. Computational biologists have long recognized that the problem of aligning nucleotides into homologous

characters is inseparable from the problem of phylogenetic inference. Indeed, Sankoffs (1975; for a historical overview see Sankoff, 2000) tree-alignment algorithm was the first formal algorithm for both multiple sequence alignment and generalized parsimony (Swofford & Maddison, 1992). Unfortunately, when systematists began analyzing DNA sequences in the 1980s, the full implications of that seminal paper were overlooked by most

workers (but not all4), and, instead of viewing phylogeny and alignment as two parts of one problem (i.e., the generalized tree-alignment problem), they applied a two-step procedure similar to the one they were accustomed to using in analyses of phenotypic characters. In the first step, the homology of nucleotide characters is fixed, either manually or algorithmically, by inserting gaps to make all sequences the same length, and the aligned nucleotides are displayed as a matrix. In the second step, searches are performed to find the tree that best explains variation in the matrix according to the chosen optimality criterion (e.g., parsimony, maximum likelihood) and evolutionary assumptions.

Insofar as the first step is intended to be independent of the second (Simmons, 2004), nucleotide correspondences are based on similarity and judged by structural or functional criteria (e.g., conservation of structural or functional properties across the aligned sequences). Manual similarity-alignments can be based on either human pattern recognition or assumptions about evolutionary mechanisms (e.g., RNA secondary structure, codon structure), but in either case a fundamental weakness is the inability to measure objectively the quality of alternative alignments. As such, objective comparison among the many possible alignments is difficult or impossible, making the preference for manual alignments notoriously subjective. Algorithmic approaches overcome this weakness by aligning sequences according to objective functions that minimize edit cost or maximize identity (Chan et al., 1992). However, insofar as the aim is to define structural or functional correspondences, the resulting similarity-alignments are often found lacking and adjusted manually, which re-introduces subjectivity and invalidates the objective function. A further complication is that different objective functions (e.g., sum-of-pairs functions, consensus functions; for review see Wheeler, 2012) can result in different optimal alignments, even under the same biological assumptions (e.g., transition, transversion, indel opening, and indel extension costs), and the basis for choosing among them is unclear. In the two-step approach, the biological assumptions used in the alignment and tree-searching steps are seldom the same, and the optimality criteria always differ. Furthermore, popular phylogenetic software for maximum likelihood (e.g., RAxML, GARLI) treats gaps as nucleotides of unknown identity (Ns; a possibly unique case in which evidence of absence is treated as absence of evidence), which excludes an entire class of evidence and can significantly distort phylogenetic results (Denton & Wheeler, 2012).

In tree-alignment, alignments are evaluated in reference to phylogenetic trees, either by optimizing sequences directly onto trees (e.g., Sankoff, 1975; Wheeler, 1996; Varn & Wheeler, 2012, 2013) or, as a heuristic approximation, by iteratively aligning sequences using a guide tree, reporting the alignment as a matrix, searching for the optimal tree for that matrix, and using the new tree to guide a new alignment (e.g., Hogeweg & Hesper, 1984; Wheeler & Gladstein, 1994; Liu et al. 2009, 2012). Because both the alignment and the tree are evaluated simultaneously under the same optimality criterion (e.g., parsimony, maximum likelihood) and biological assumptions, nucleotide correspondences relate directly and explicitly to evolutionary transformation events, i.e., homology (note that this does not hold in the approximation of Liu et al., 2009, 2012; see Denton & Wheeler, 2012). Accordingly, tree-alignment can identify phylogenetic hypotheses that are significantly more optimal than the two-step procedure (e.g., Wheeler, 1994, 1996, 2007; Whiting et al., 2006; Wheeler & Giribet, 2009). As with other methods of alignment, it is possible to display aligned nucleotides as a matrix (Wheeler, 2003); however, this

4. Felsenstein (1988, p. 525): Sankoff et al. [1973] applied a method, later described by Sankoff & Rousseau [1975] and Sankoff [1975], that performs alignment of sequences at the same time as it estimates the phylogeny by minimizing a weighted count of substitutions and deletion/ insertion eventsThis process is computationally intensive but will receive more attention when sequence aligners realize, as they must, that multiple-sequence alignment is best carried out with explicit reference to the phylogeny and that one cannot simply treat all sequences symmetrically, when some may be near-duplicates of others. The realization of this will have a large impact on multiple-sequence alignment and may cause some embarrassment when it is noted that David Sankoff and his colleagues understood the matter clearly in 1973. Zootaxa 3825 (1) 2014 Magnolia Press 13

so-called implied alignment differs fundamentally from those discussed above in that it depicts the historical, PADIAL ET AL.14 Zootaxa 3825 (1) 2014 Magnolia Press

evolutionary relationships among nucleotides, not their structural or functional similarity (Wheeler, 2003; Giribet, 2005).

Failure to recognize the distinction between similarity-alignments and tree-alignments can lead to serious logical errors, and it is incorrect to assess either approach by the others criteria (as exemplified by Hickson et al., 2000). For example, similarity-alignments are often constrained to preserve structural and/or functional characteristics such as codon reading frames. If the goal is to visualize structural or functional similarity across extant taxa, then this constraint is appropriate. However, if the goal is to explain shared structural or functional similarity by identifying homologous nucleotides related through evolutionary transformation events, then it is not. Gaps are not nucleotides of unknown identity (Ns), as they are treated by most phylogenetic software; they are symbolic representations of the absence of any nucleotide (i.e., they do not exist) and serve as mere placeholders to allow homologous nucleotides to be visualized in matrix format. As such, gaps have no bearing on the structural or functional viability of the extant sequences; to understand the structural or functional implications of a given alignment, the hypothetical ancestral sequences must be examined for the effects of both indels, which can alter reading frame and secondary structure, and substitutions, which can also result in missense and nonsense codons and alter secondary structure (e.g., non-Watson-Crick pairing within stems). Tree-alignment matrices commonly place gaps within functional blocks, indicating that indel events contributed to the evolution of those blocks. As Lytynoja & Goldman (2008, p.1635) summarized succinctly, the resulting alignments may be fragmented by many gaps and may not be as visually beautiful as the traditional alignments, but if they represent correct homology, we have to get used to them.

Similarly, because matrix representations of tree-alignments depict evolutionary transformation series, they are not necessarily effective at identifying structural and functional similarities across terminals. Structural patterns can be less evident due to gaps within functional blocks in tree-alignment matrices. Further, nucleotides in the same sequence position that are separated evolutionarily by indels form non-homologous transformation series in tree-alignments and, therefore, are correctly depicted in different columns in the tree-alignment matrix (Figure 1); however, this separation in the matrix obscures the structural and functional equivalence of these nucleotides, which is correctly depicted by merging the separate, non-homologous transformation series into a single column, as shown in the similarity-alignment matrix. Consequently, workers must consider their objectives carefully when choosing an alignment method: similarity-alignment for visualizing structural and functional similarities among terminals, tree-alignment for discovering the evolutionary transformation events that gave rise to (and therefore explain) those structural and functional similarities.

FIGURE 1. An example showing one of the differences between similarity- and a tree-alignments of the same data. Nucleotides in the same sequence position but separated evolutionarily by insertion/deletion events are non-homologous but functionally equivalent. The tree-alignment matrix depicts the homology relationship of the nucleotides clearly but obscures their functional equivalence, whereas the similarity-alignment matrix depicts the functional equivalence of the nucleotides clearly but obscures their homology relationship.

A A A A

AA

AA A

A

AAAA

Tree-Alignment Matrix Similarity-Alignment Matrix

Models and model selection SYSTEMATICS OF BRACHYCEPHALOIDEA

The idea that some characters are better than others for discovering relationship has a long pedigree that descends directly from Owen's (1843) pre-evolutionary notions of analogy and homology. We identify two ways in which the quality of characters is commonly assessed and used in phylogenetic analysis. First, quality is assessed in terms of the observers ability to unambiguously individuate character-states and group them into transformations series. Accordingly, good characters in frogs would include the presence/absence of direct development, tadpole transport by parental nurse frogs, and teeth, whereas those that are more ambiguously individuated (e.g., shape of the frontoparietal fontanelle; relative length of toes III and V, wherein the same states could arise through different transformations) or grouped into homologous transformation series (e.g., the various morphologies of the supplementary elements of the submandibular musculature) are less good. It was this line of reasoning that formed the basis for Neffs (1986; see also Haszprunar, 1998; Vogt, 2002) proposal to weight characters by asking, how much do we think we know about this character? rather than how much is this character intrinsically capable of telling us? However, its unavoidable subjectivity, both philosophically (the focus is not on the objective ability of evidence to refute hypotheses, but instead on what has been learned about the evidence) and operationally (the determination of specific values to quantify how much we think we know) has prevented it from being widely adopted.

The second way character quality is commonly assessed and applied in phylogenetic analysis is by assessing a characters intrinsic reliability. A priori weighting, whereby more reliable characters or changes are attributed greater weight than less reliable ones, was criticized early and often due to its subjectivity (e.g., Sokal & Sneath,

1963; Kluge & Farris, 1969) and several efforts have aimed to objectify the approach5. It has often been argued that complexity and functional or adaptive importance indicate how easy or difficult it is for characters or character-states to arise or change (e.g., Le Quesne, 1974; Hecht & Edwards, 1976). However, it is well established that simple genetic mutations can have highly complex and major phenotypic consequences such that apparently complex and functionally important changes may be achieved quite simply (e.g., Eizirik et al., 2003; Theissen, 2009; Uy et al., 2009; Nadeau & Jiggins, 2010). Cracraft (1981) discussed in detail the subjectivity and irrelevance of using functional and adaptive criteria for discriminating characters for phylogenetic inference. Similarly, even though functional constraints on some genes and sequence positions might make certain changes implausible, many such assumptions are rejected by an ever-increasing variety of mechanisms that permit exceptions, including post-transcriptional editing (Bock, 2000), altered genetic codes (Abascal et al., 2012), frameshift tolerance (Russell & Beckenbeck, 2008; Masuda et al., 2010), network rewiring (Kim et al., 2012), and wobbling and superwobbling (Alkatib et al., 2012), among many others.

Alternatively, Farris (1966, 1970; see also Kluge and Farris, 1969) proposed to weight characters inversely according to their within-population variation, the argument being that traits that vary extensively within a population are more likely to evolve at a higher rate and vary among species, whereas traits that are more conserved within a population are likely to evolve more slowly and be more conserved among species, thereby constituting more reliable characters. Although this potentially offers an objective, data-driven method for a prioricharacter weighting, the necessary data are rarely available and, more importantly, even if on average a correlation exists between variation within and among groups (Kluge & Kerfoot, 1973), there is no biological or evolutionary law that requires a given trait to maintain the same amount of within-population variation over time and across lineages. Given its many drawbacks in both theory and practice, a priori reliability weighting was overwhelmingly rejected in parsimony analyses.

Despite the many criticisms of methods that differentially weight characters based on their intrinsic reliability, maximum likelihood methods weight according to reliability by modeling the process of character evolution. The putative problem of superimposed substitutions (multiple hits, saturation) in DNA sequences has probably received most attention (e.g., Simon et al., 1994; Swofford et al., 1996; Xia et al., 2003; see also Wenzel & Siddall, 1999), but modeling extends well beyond this to accommodate potentially any problem of so-called non-phylogenetic signal (Philippe et al., 2011). Whether statistical models are mechanistic, specifying parameter values based on empirical data obtained previously, or empirical, estimating values directly from the data to be analyzed, the

5. Another approach is to assess and weight reliability according to homoplasy (Farris, 1969; Goloboff, 1993) or support (Farris, 2001). However, these methods are rarely applied to molecular data and therefore we do not address them beyond noting that the results of these methods can only deviate from those obtained under equal weights by increasing the amount of homoplasy and number of ad hoc hypotheses of transformation, i.e., by selecting a less parsimonious tree. Zootaxa 3825 (1) 2014 Magnolia Press 15

parameters included in a given model are specified a priori on the basis of external knowledge claims, wherein lies PADIAL ET AL.16 Zootaxa 3825 (1) 2014 Magnolia Press

modelings major operational shortcoming as objectively assessing character reliability. Instead of developing models from painstaking empirical research into, for example, the chemical laws that govern mutations and molecular interactions, DNA repair efficiency, metabolic rate, generation time, body size, population size, and selection pressures, phylogenetic models are merely biologically inspired (Huelsenbeck et al., 2011)speculations based on what people believe to be more-or-less plausible. One need only reflect on the complexity of the human genome and the results that are emerging from the ENCODE Project Consortium (Dunham et al., 2012) to see the futility and inherent subjectivity of biological inspiration.

Indeed, although biological realism has been claimed as a critical strength of modeling (e.g., Huelsenbeck & Crandall, 1997; Huelsenbeck & Rannala, 1997) it has never been more than a slogan; models have always been defined more by the simplicity of mathematical calculations and avoidance of statistical inconsistency than biological realism (Farris, 1999). The lack of concern for realism is perhaps best illustrated by the fact that to this day the most popular methods and software for model selection (e.g., ModelTest) and statistical phylogenetic analysis (e.g., RAxML, GARLI) fail to model indels and instead treat gaps as if they were nucleotides of unknown identity, despite the well-recognized evolutionary importance of indels (e.g., Britten et al., 2003; Wetterbom et al., 2006). Although some authors remain skeptical and acknowledge that models rely on false or at best untested assumptions (e.g., Fontanillas et al., 2007; Ho, 2009; Ho et al., 2011), such fine print is usually overlooked by end users. A generation ago, systematists rejected this kind of a priori subjectivism as being inconsistent with core scientific principles, and the fact that it has gained such popularity is more a reflection of the degree to which those principles have been set aside than progress in understanding of molecular evolution.

In the absence of objective model specification, the field has turned to model selection criteria to objectively choose among the subjectively formulated models. It is important to note that model selection methods optimize statistical selection criteria (in essence, balancing the tradeoff between bias and variance through the parsimonious inclusion of parameters) regardless of the statistical adequacy or biological legitimacy of the candidate models, which is why statisticians are careful to caution If a particular model (parameterization) does not make biological sense, it should not be included in the set of candidate models (Burnham & Anderson, 1998, p. 8, italics in original) and recommend extensive a priori probing. This is especially germane considering that the practical effect of biologically inspired models (Huelsenbeck et al., 2011) is to impose constraints on Tuffley & Steels (1997) No-Common-Mechanism modelone of the parsimony-equivalent likelihood models. Moreover, given that indel formation is likely the most rapid and significant form of sequence change (mutation) in eukaryotic evolution and probably bacterial evolution (Britten et al., 2003, p. 4665), any model that ignores this class of event or, worse still, treats gaps as nucleotides of unknown identity clearly does not make biological sense. In addition to empirical evaluation of model constraints, we suggest that a priori probing also address such considerations as the uniqueness of history, the applicability of models to idiographic problems, and the role of subjectively defined models in science, as well as the philosophical foundations of model testing itself (cf. Burnham & Anderson, 2004). As George Box counseled, It is inappropriate to worry about mice when there are tigers abroad (Box, 1976, p. 792).

Central to the philosophy of model testing is the assertion that there are no true models (Burnham & Anderson, 2004), a sentiment captured succinctly by George Boxs more famous quote (usually attributed to the same 1976 paper) all models are wrong, but some are useful and echoed by systematists (e.g., Posada & Buckley, 2004; Sullivan & Joyce, 2005). Consequently, by embracing this approach to science, systematists abandon scientific realism in favor of instrumentalism, an anti-realist view that rejects scientific theories as candidates for truth or reference and construes methods and hypotheses as mere instruments that are more or less useful. Instrumentalism is potentially appealing because it avoids problems that realism must face square on; however, in failing to resolve the problems faced by realism, the resulting knowledge lacks any claim to reality and must instead be defended by answering the question useful for what? without resorting to tautology or parochial goals (e.g., publishing). In applied sciences like economics, engineering, and medicine, the answer is clear. However, what does useful mean in a science that aims to discover unique historical events, and why should parsimony in the tradeoff between bias and variance take precedence over parsimony in the postulation of events?

Maximum likelihood and Brachycephaloidea

The gulf between the statistical rhetoric of theoretical papers and the reality of most empirical studies is vast, as exemplified by the recent maximum likelihood analyses used to erect the current taxonomy of terraranas,

specifically Heinicke et al. (2007, 2009), Hedges et al. (2008a), Padial et al. (2009), Pyron & Wiens (2011), SYSTEMATICS OF BRACHYCEPHALOIDEA

Canedo & Haddad (2012), and Pinto-Snchez et al. (2012). In examining these studies, we have identified five fundamental analytical problems that contravene the theoretical foundations of maximum likelihood inference.

1. Application of the optimality criterion.Heinicke et al. (2007) and Hedges et al. (2008a) based their inferences on analyses of three overlapping datasets. Analysis 1 had the most terminals (280 and 350, respectively) scored for 350 bp of 12S and 800 bp of 16S, analysis 2 had fewer terminals (146 and 216) scored for the entire 2.5

kb heavy strand transcription unit 1 fragment (12S + tRNAval + 16S; H1), and analysis 3 had the fewest terminals (65 and 80) sequenced for the most data (H1, and the two nuclear genes Rag-1 and Tyrosinase). However, they never combined all their data into a single analysis, which means they never actually searched for the maximum likelihood solution for their data. Neither Heinicke et al. (2007) nor Hedges et al. (2008a) offered a justification for this procedure, but decreased accuracy due to missing data does not seem to have been the motivation, as Hedges et al. (2008a, p. 9) clarified that some of the species included in their analyses lacked substantial amounts of data. Nor did Heinicke et al. (2007) or Hedges et al. (2008a) provide a rule for resolving conflict between the different results, stating only that the species-rich analyses [1 and 2] provided guidance for taxonomic decisions at lower levels (e.g., species groups and series) whereas the gene-rich analyses [2 and 3] provided guidance for decisions at higher levels, although all three analyses were consulted in many cases (Hedges et al. 2008a, p. 11), meaning that results that did not conform to expectations could be waved away in favor of one of the other analyses. For example, Haddadus is the sister of Eleutherodactylidae in analysis 1 but is the sister of Craugastor in analyses 2 and 3. Without comment, Hedges et al. (2008a) referred Haddadus to Craugastoridae instead of Eleutherodactylidae and then used its phylogenetic position to interpret the role of ancestral body size in large adaptive radiations (p. 137). Not only does this violate basic statistical assumptions, it is also precisely this sort of subjective cherry-picking among hypotheses that explicit optimality criteria are meant to avoid.

In order to assess whether Hedges et al.s (2008a) partitioning of the data into different datasets led them to recognize taxa not supported by the overall evidence, we combined their three datasets and analyzed them in GARLI (Zwickl, 2006; for more details about the procedure followed for maximum likelihood inferences in GARLI see below). The optimal topology (log likelihood = -203567.0836; TreeBase accession http://purl.org/phylo/treebase/phylows/study/TB2:S15350) is largely congruent with Hedges et al.s (2008a) analysis 3 with respect to the family-group taxa (including the placement of Haddadus as sister to Craugastor) and analysis 1 and 2 with respect to genera, subgenera, and species groups and series. All their family-group taxa are monophyletic, including Strabomantidae and Strabomantinae, as are all their genus-group taxa except the subgenus Pristimantis, because P. dendrobatoides, P. rozei, and P. urichi are the sister taxon of Hypodictyon and the rest of species of the subgenus Pristimantis. The most egregious difference that results from the combined analysis is the placement of Ceuthomantis smaragdinus (as unknown anuran sp.), which is placed outside the Brachycephaloidea as the sister of Dendrobates sylvaticus (presently Oophaga sylvatica; Grant et al., 2006). Given that the maximum likelihood solution for the entire dataset supports Hedges et al.s (2008a) major conclusions, whatever concerns led those authors to sacrifice analytical principles to base their conclusions exclusively on separate analyses seem unwarranted.

2. Alignment.Despite tree-alignments clear advantages over similarity-alignment for phylogenetic inference and the wide availability of software for both parsimony and statistical optimality criteria (e.g., Hogeweg & Hesper, 1984; Wheeler & Gladstein, 1993; Wheeler, 1996; Edgar & Sjlander, 2003; Lunter et al., 2003; Wheeler et al., 2003; Fleiner et al., 2005; Redelings &Suchard, 2005; Novk et al., 2008; Rivas &Eddy, 2008; Yue et al., 2009; Varn et al., 2010), none of the recent papers on the phylogeny of terraranas employed this approach. Without discussion or justification, Heinicke et al. (2007), Hedges et al. (2008a), and Canedo & Haddad (2012) used Clustal (Thomson et al., 1994; Larkin et al., 2007), Heinicke et al. (2009) used MUSCLE (Edgar, 2004), Pyron & Wiens (2011) used Clustal and MUSCLE, and Padial et al. (2009) and Pinto-Snchez et al. (2012) used MAFFT (Katoh et al., 2005); all of these methods seek to minimize the weighted pairwise distance summed over all sequence pairs in the multiple sequence alignment. And all of these studies except Padial et al. (2009), which only used ribosomal DNA, forced gaps to correspond with codon reading frame of the observed sequences, even though gap placement has no bearing on the reading frame of extant sequences and hypothetical ancestral sequences were not examined for missense or nonsense codons in any of the studies. Moreover, all of the studies except Padial et al. (2009) performed manual adjustments of their algorithmic alignments, often removing poorly conserved regions. Perhaps most importantly, all of these studies used model selection and tree searching software that fails to model indel evolution and instead treats gaps as nucleotides of unknown identity (Ns). Zootaxa 3825 (1) 2014 Magnolia Press 17

3. Model selection.Although the importance of model selection in statistical phylogenetic inference is widely PADIAL ET AL.18 Zootaxa 3825 (1) 2014 Magnolia Press

recognized (e.g., Johnson & Omland, 2004; Posada & Buckley, 2004; Sullivan & Joyce, 2005), Heinicke et al.(2007), Hedges et al. (2008a), and Pyron & Wiens (2011) did not perform any analysis to select the optimal models for their data. Padial et al. (2009) used the Akaike Information Criterion (AIC) in ModelTest v.3.7 (Posada & Crandall, 1998) to identify the best model (general-time-reversible + gamma + proportion of invariant sites; GTR + GAMMA + I), which they applied in their phylogenetic analysis. Heinicke et al. (2009), Canedo & Haddad (2012), and Pinto-Snchez et al. (2012) used ModelTest v.3.7 or jModelTest (Posada, 2008) to identify the best models (usually GTR + GAMMA + I), but none of the studies actually used the selected models in their maximum likelihood analyses. Instead, they used the GTR + GAMMA model (or approximations). If the authors were opposed to GTR + GAMMA + I a priori due to theoretical objections (e.g., Stamatakis, 2008), then those objections should have been provided and the model should have been excluded from the set of candidates. Otherwise, the model that best accounts for their data should have been used. In addition to using an underparameterized model, Canedo & Haddad (2012, p. 612) justified using GTR + GAMMA when it was an overparameterized model by citing Lemmon & Moriarty (2004) and Kelchner & Thomas (2007) that overparameterization may have little influence on the resulting topology, despite the cited authors clear warnings that the potential impacts of estimating more parameters than warranted should not be ignored. Under- and over-parameterization also obtain when data are partitioned into too few or too many partitions (McGuire et al., 2007; Li et al., 2008; Lanfear et al., 2012; Leavitt et al., 2013). However, Pinto-Snchez et al. (2012) were the only authors who statistically evaluated their a priori partition schemes. All other studies simply assumed a single partition scheme. Finally, none of the studies evaluated model adequacy, meaning that the selected models might be relatively better than others but still not provide a significantly good fit to the data (Ripplinger & Sullivan, 2010).

4. Heuristic searches.The statistical strengths of maximum likelihood dissolve if heuristic searches are unable to find the maximum likelihood solutiona non-trivial consideration given that maximum likelihood tree searches are thousands of times slower than parsimony tree searches (Sanderson & Kim, 2000). Given the large numbers of terminals in most of these studies, the heuristic searches were extremely superficial and are unlikely to have discovered global optimathe sole exception being those of Heinicke et al. (2009), which analyzed only 46 terminals. Despite the differences in datasets sizes (46362 terminals), Heinicke et al. (2007), Hedges et al.(2008a), Heinicke et al. (2009), and Canedo & Haddad (2012) all performed the same maximum likelihood search of 100 runs in RAxML (Stamatakis et al., 2006), each run consisting of an initial random addition sequence parsimony tree swapped using the Lazy Subtree Rearrangements algorithm (lazy SPR; Stamatakis et al., 2005, 2007), which confines SPR swapping to the vicinity of the clipped branch instead of performing global SPR swapping. No reason was given by any of the authors for using the 100-replicate lazy SPR search strategy as their standard, and although such procedures may be effective as components of an overall search strategy (e.g., Goloboff, 1999), on their own they are quite unreliable (Morrison, 2007). Pyron & Wiens (2011) and Pinto-Snchez et al. (2012) used the Rapid ML Search procedure (Stamakis et al., 2008), which performs lazy SPR on every 5th bootstrap replicate tree (totaling 20 trees) using the GTR + CAT model (a GAMMA approximation using a fixed number of rate categories; Stamatakis et al., 2006), rediagnoses the resulting trees using GTR + GAMMA and swaps the best 10 trees again (similar to a reweighting step in the parsimony ratchet; Nixon, 1999), and then swaps the best of the resulting trees using less-lazy lazy SPR. Although that heuristic is more effective for large datasets than standard lazy SPR searches (Stamakis et al., 2008), given the size of the Pyron & Wiens (2011) and Pinto-Snchez et al. (2012) datasets, its adequacy is questionable. Similarly, Padial et al. (2009) ran 100 replicates in GARLI (Zwickl, 2006), which, under default parameters, also implements stepwise addition and local SPR searches with reattachments restricted to a maximum of six nodes from the original location of a pruned branch.

5. Support.Inadequate heuristic searches also affect estimates of support. All recent studies of Brachycephaloidea exclusively used non-parametric bootstrap resampling frequencies (Efron, 1979; Felsenstein, 1985) as clade support measures in maximum likelihood. Heinicke et al. (20